High-performance synthetic aperture radar (SAR) requires precise knowledge of the relative motion between a radar system (e.g., an airborne radar system) and a target scene. This is most often accomplished with a global positioning system (GPS)-aided navigation system, e.g., an inertial navigation system (INS). Integral to an INS is an inertial measurement unit (IMU), whereby the IMU is typically composed of three orthogonal accelerometers and three orthogonal rate gyroscopes. The task of the GPS is to provide absolute references for correcting errors which can occur at the IMU owing to noise, drift, etc.
Correction of IMU and subsequent INS motion information is often performed via a linear quadratic estimation (LQE) such as a Kalman Filter (KF), an Extended Kalman Filter (EKF), etc., which combines the GPS and IMU data to estimate errors and corrections, and to achieve a blended motion measurement solution. Such an algorithm and its implementation are frequently referred to collectively as the “navigator”. The correction of IMU and subsequent INS motion information is termed “alignment” of the navigator.
In the absence of GPS-aiding, instrument noise in the INS can cause drifts in the motion data, which can lead to inaccurate estimates of position, velocity, angular orientation, etc.
In particular, an error in the velocity estimate can yield an azimuth scaling error in a SAR image, as well as a mis-focus or blurring in the image. At non-broadside squint angles, a velocity error can be manifested as an unknown and undesired Doppler shift, further manifesting as an additional position shift and illumination error, with attendant deleterious effects on radar cross section (RCS) estimation.